Spatial context-aware user mention behavior modeling for mentionee recommendation.

As one of the most common user interactive behaviors in many social media services, mention plays a significant role in both user interaction and information cascading. While an increasing line of work has focused on analyzing the mention mechanism for information diffusion, the essential problem of mentionee recommendation from the perspective of common users, i.e., how to find mentionees (mentioned users) who are most likely to be notified by a mentioner (mentioning user) for knowing a post, has been seldom investigated. This paper aims to develop personalized recommendation techniques to automatically generate mentionees when a user intends to mention others in a post. After analyzing real-world social media datasets we observe that users' mention behaviors are influenced by not only the semantic but also the spatial context factors of their mentioning activities, which motivate the needs for spatial context-aware user mention behavior modeling. In light of these, we proposed a joint probabilistic model, named Spatial COntext-aware Mention behavior Model (SCOMM), to simulate the process of generating users' location-tagged mentioning activities. By exploiting the semantic and spatial context factors in a unified way, SCOMM was able to reveal users' preferences behind their mention behaviors and provide a knowledge model for accurate mentionee recommendations. Furthermore, we designed an Item-Attribute Pruning (IAP) algorithm to overcome the curse of dimensionality and facilitate online top-k query performance. Extensive experiments were conducted on two real-world datasets to evaluate the performance of our methods. The experimental results demonstrated the superiority of our approach by making more effective and efficient recommendations compared with other state-of-the-art methods.